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import copy
import torch
def fuse_conv_bn_eval(conv, bn):
assert(not (conv.training or bn.training)), "Fusion only for eval!"
fused_conv = copy.deepcopy(conv)
fused_conv.weight, fused_conv.bias = \
fuse_conv_bn_weights(fused_conv.weight, fused_conv.bias,
bn.running_mean, bn.running_var, bn.eps, bn.weight, bn.bias)
return fused_conv
def fuse_conv_bn_weights(conv_w, conv_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b):
if conv_b is None:
conv_b = bn_rm.new_zeros(bn_rm.shape)
bn_var_rsqrt = torch.rsqrt(bn_rv + bn_eps)
conv_w = conv_w * (bn_w * bn_var_rsqrt).reshape([-1] + [1] * (len(conv_w.shape) - 1))
conv_b = (conv_b - bn_rm) * bn_var_rsqrt * bn_w + bn_b
return torch.nn.Parameter(conv_w), torch.nn.Parameter(conv_b)
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